1. Field of the Invention
The present invention relates to the field of digital image processing, and particularly relates to an object detection method and an objection detection device.
2. Description of the Related Art
An object detection method has been widely used up to now. In particular, in the field of computer vision, the detection and tracking of an object has become a hot research topic. The reason is that it may automatically sense the key information provided by a specific object for a computer, and may provide a bottom-layer service to some upper-lay applications (e.g., recognizing the activity of an object and understanding the relevant scenario). For example, a method of detecting a vehicle on a road in a driver assistance system may effectively detect or recognize the existence or non-existence, the distance, the velocity, and the orientation of the vehicle, so as to be able to assist a driver to avoid a traffic accident.
At present, there are mainly two kinds of object detection methods.
On the one hand, there is a kind of object detection method of detecting an object on the basis of a disparity map (or called a “depth map”). For example, it may detect, on the basis of the disparity map, a pedestrian, a vehicle, a fence, etc. Since the object detection method on the basis of the disparity map is not easily influenced by an environmental factor such as lighting, it is very robust to the change of environment. In addition, this kind of object detection method is also called a “disparity map based object detection method”.
However, sometimes due to the performance restriction of a camera or the characteristic of a corresponding scenario, the camera may only obtain a disparity map in which the disparities are relatively sparse. Hereinafter this kind of disparity may is also called a “sparse disparity map”. Here it should be noted that a sparse disparity map refers to one in which there are only a few pixel points having valid disparities (hereinafter, a pixel point having a valid disparity is also called a “disparity point”) discontinuously existing on the boundary of an object. The reason of the occurrence of this kind of phenomenon may be that some stereo matching algorithms only match strong texture based pixel points or only adopt high confidence degree based pixel points. As a result, this kind of sparse disparity map may result in difficulty detecting or incompletely detecting an object such as a pedestrian or a vehicle.
On the other hand, there is a kind of object detection method of detecting an object on the basis of an original image (e.g., a grayscale or a color image). For example, it may utilize abundant grayscale or color information to detect various objects. In addition, this kind of object detection method is also called an “original image based object detection method”.
However, in this kind of object detection method, the grayscale or color feature may be easily influenced by an environmental factor such as lighting. As a result, the visual feature of a same object may change in different frames (i.e., images), thereby resulting in not being able to match the objects, eventually resulting in the object missing detection.